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The relationship between poverty and the infection and case-fatality rates of COVID-19 has emerged as a controversial but understudied topic. In previous studies and reports from the UK and US evidence emerged that poverty-related indicators had a significant statistical effect on case and mortality rates on district level. For Germany, it has largely been assumed that poverty is an equally relevant factor influencing the transmission rates of the outbreak. This was mostly due to anecdotal evidence from local outbreaks in meat processing plants and reported incidents of infection clusters in poorer city districts. This paper addresses the lack of statistical evidence and investigates thoroughly the link between poverty-related indicators and detected infection and mortality rates of the outbreak using multivariate, multilevel regression while also considering the urban-rural divide of the country. As proxies for poverty the unemployment rate, the per capita presence of general practitioners (physicians), per capita GDP, and the rate of employees with no professional job training is evaluated in relation to the accumulated case and mortality numbers on district level taken from RKI data of June and July 2020. Interestingly, the study finds no general evidence for a poverty-related effect on mortality for German districts during the first wave in the first half of 2020. Furthermore, only employment in low qualification jobs approximated by the job training variable consistently affected case numbers in urban districts in the expected direction.
Building on previous quantitative and qualitative research on cross-country differences and similarities in regulation of religion, this paper employs data for 2000 and 2014 from the third round of the Religion and the State project and uses various cluster analysis techniques to identify country clusters based on the form in which countries regulate religion. The analysis separates between democratic and authoritarian countries. We further study if and how the clustering of the countries changes depending on the employed indicators and the employed cut-off points in defining democracy and autocracy. Overall, the results demonstrate the potential and limits of empirical classifications. In addition to the methodological and descriptive contribution, the results are compared and contrasted with previous work on state-religion relationships.
Our contribution examines two questions regarding the internal security policies of 28 European countries: First, the question which different internal security conceptions regarding crime management exist and second, how countries cluster along these conceptions. As data foundation, we use a two-dimensional approach examining the dimensions of capabilities and punitivity with two variables for each. For the dimension of capabilities, we utilize the spending share of government budget for internal security and the relative number of police officers and for the punitivity dimension, we consider average prison terms and the share of alternatives to conventional incarceration. By using this data in combination with modern clustering techniques, we prove that our results are stable and cohesive despite the wide variety of different methods and clustering techniques deployed, which include state-of-the-art unsupervised learning algorithms adapted from big data frameworks. By also including most Eastern European Countries in a comparative European setup for the first time, we identify five different clusters, namely a Western and Central European Cluster, a liberal Scandinavian cluster, two different Southern and Eastern European clusters with high capabilities and very uneven levels of punitivity, and one cluster with special cases with very infrequent use of alternatives to conventional punishment.
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